The dataset used in this experiment is generated by Intensive Care Unit patient medical record data (MIMIC-III) of Beth Israel Deaconess Medical Center in the United States [1]}. More than 20000 patient samples in MIMIC-III were extracted from the dataset, covering more than 4000 kinds and a total of more than 20 million multimodal irregular time-series data. In the experiment, the dataset is divided into training set,validation set and evaluation set, with a ratio of \(7:1:2\) .
Table REF  shows the data distribution of the dataset, which is divided into two classes. All experiments were implemented by Pytorch [2]}, optimized by Adam optimization algorithm [3]}, with the learning rate of 0.0001 and the other parameters are selected as default parameters. We set the random number seed to 1 to ensure the repeatability of the experimental results. Unless otherwise specified, \(d_{model}\)  (the dimension after features coding) is 256, the batchsize is 128. The detailed parameter settings of different experiments are described below. All the experiments are conducted on a single Nvidia RTX 3090 GPU (24GB memory), which is sufficient for all the baselines.
